// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "../funcs/top_k_function_cuda.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/common/bfloat16.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/top_k_grad_kernel.h"

namespace phi {

template <typename T, typename Context>
void TopkGradKernel(const Context& dev_ctx,
                    const DenseTensor& x,
                    const DenseTensor& indices,
                    const DenseTensor& out_grad,
                    const Scalar& k_scalar,
                    int axis,
                    bool largest,
                    bool sorted,
                    DenseTensor* x_grad) {
  const auto& in_dims = x.dims();
  const auto& out_dims = indices.dims();

  int k = k_scalar.to<int>();

  // get the real the axis and the k
  if (axis < 0) {
    axis += in_dims.size();
  }
  const int& raw_height = in_dims[axis];

  // allocate the cuda memory for the x_grad
  T* x_grad_data = dev_ctx.template Alloc<T>(x_grad);
  const T* out_grad_data = out_grad.data<T>();
  const int64_t* indices_data = indices.data<int64_t>();

  if (in_dims.size() == 0) {
    phi::Copy<Context>(dev_ctx, out_grad, dev_ctx.GetPlace(), false, x_grad);
    return;
  }

  int pre, n, post;
  phi::funcs::GetDims(in_dims, axis, &pre, &n, &post);

  // calculate the block and grid num
  auto ComputeBlockSize = [](int col) {
    if (col > 512)
      return 1024;
    else if (col > 256 && col <= 512)
      return 512;
    else if (col > 128 && col <= 256)
      return 256;
    else if (col > 64 && col <= 128)
      return 128;
    else
      return 64;
  };
  int block_size = ComputeBlockSize(post * k);
  int max_threads = dev_ctx.GetMaxPhysicalThreadCount();
  const int max_blocks = std::max(((max_threads - 1) / block_size + 1), 1);
  int grid_size = std::min(max_blocks, pre);

  // launch the cuda kernel to assign the grad
  phi::funcs::AssignGradWithAxis<T>
      <<<grid_size, block_size, 64 * 4, dev_ctx.stream()>>>(
          out_grad_data, indices_data, x_grad_data, pre, post, n, k);
}

template <typename T, typename Context>
void TopkV1GradKernel(const Context& dev_ctx,
                      const DenseTensor& x,
                      const DenseTensor& indices,
                      const DenseTensor& out_grad,
                      const Scalar& k_scalar,
                      DenseTensor* x_grad) {
  TopkGradKernel<T, Context>(
      dev_ctx, x, indices, out_grad, k_scalar, -1, true, true, x_grad);
}
}  // namespace phi

PD_REGISTER_PLUGIN_KERNEL(topk_grad,
                          iluvatar_gpu,
                          ALL_LAYOUT,
                          phi::TopkGradKernel,
                          float,
                          int,
                          int64_t,
                          phi::dtype::float16,
                          phi::dtype::bfloat16) {}

PD_REGISTER_PLUGIN_KERNEL(topk_v1_grad,
                          iluvatar_gpu,
                          ALL_LAYOUT,
                          phi::TopkV1GradKernel,
                          float,
                          int,
                          int64_t,
                          phi::dtype::float16,
                          phi::dtype::bfloat16) {}
